System and method for asset-agnostic wireless monitoring and predictive maintenance of deployed assets

ABSTRACT

Systems and methods for asset monitoring utilize a network, one or more data servers, one or more asset-monitoring modules, and one or more beacon modules to monitor assets deployed across a facility. The systems and methods are asset-agnostic and are able to provide statistical forecasts and degradation model for each asset monitored. The systems and methods may further apply real-time regression, including Bayesian updating techniques to provide real-time corrections to forecasts and degradation models.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/450,365, filed Jan. 25, 2017, the entire contents of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to monitoring and maintaining assets at afacility. More particularly, the present invention employs wirelessnetworking to design an asset-agnostic monitoring system that providesalerts and recommendations on deployed assets.

Background of the Related Art

In recent years, the shortcomings of medical equipment management havecome under increased scrutiny. At least 50,000 serious adverse eventsare caused by medical devices annually in the Unites States, with over3,000 of such events being patient deaths. These statistics arecollected through voluntary reporting; hence the actual amount of harmcaused by medical device failure may be much greater than indicated. Inresponse to this, the U.S. Food and Drug Administration has called forimprovement in medical device surveillance across the country.

Healthcare providers also face significant challenges in developingsmart, effective maintenance strategies. Those in charge of managinghospital equipment, called clinical engineers, traditionally performpreventative maintenance on medical equipment at scheduled intervalsfollowing original equipment manufacturer (OEM) recommendations. This isinherently inefficient, as degradation results not directly from thepassage of time, but from equipment use and suboptimal environmentalconditions. Moreover, the desire to curtail liability and strengthenparts sales gives manufacturers the incentive to recommendoverly-frequent preventative maintenance tasks. The rapid proliferationof equipment in hospitals has amplified these inefficiencies, makingmaintenance compliance prohibitively expensive (and oftentimesunattainable), and finally compelled the key oversight groups, theCenters for Medicare and Medicaid Services (CMS) and The JointCommission (TJC), to allow hospitals to enact maintenance policies thatdeviate from OEM recommendations so long as patient safety is preserved.The freedom to utilize these customized policies, termed alternativeequipment maintenance (AEM) programs, has thrown the industry into astate of transformation, sending clinical engineers everywhere on a huntfor data that will support the formation of reliable, sustainable AEMprograms.

SUMMARY OF THE INVENTION

The solution to these many challenges may rest in Internet of Things(IoT) technology, whereby physical objects are equipped withwireless-communication-enabled electronics and brought into a network.As wireless networking devices have become smaller and morepower-efficient, it has suddenly become possible to collect vast amountsof data on key metrics of interest. Such devices may be outfitted withsensors and deployed in a variety of settings; helping to track shippingcontainers as they move across oceans, monitoring criticalinfrastructure for failure-inducing conditions, and enablingenergy-efficient “smart” homes. If properly leveraged, IoT may prove tobe a wellspring of data needed for the formation of effective AEMprograms, a tracking aid that allows nurses to reclaim precious hourslost searching, and an unrelenting, comprehensive observer capable ofpreventing dangerous equipment malfunctions before they materialize.Going further, any asset might be brought under the fold of such asystem, so that the benefits of IoT spread past diagnostic and imagingequipment, and extend to beds, tool carts, refrigerators, computers,etc. IoT truly has the potential to revolutionize asset managementwithin healthcare facilities and beyond.

Accordingly, it is an object of the invention to monitor assets deployedthrough a facility using networked sensors and provide alerts,maintenance recommendations, and reports using the collected data.

The system is comprised of two main subsystems: the asset-monitoringnetwork and the data analysis algorithm. The former is a wirelessnetwork of sensor-equipped modules affixed to equipment distributedwithin a facility. These modules collect data relevant to the operationand environment of equipment and transmit this data to a central serverwithin the facility. Data processing may occur directly on this server,or it may occur remotely (i.e., through “cloud computing”) with theserver acting as a conduit between the asset-monitoring network and aweb-based application. The other major subsystem, the data analysisalgorithm, combines data collected by the asset-monitoring network withuser-provided information to form a statistical degradation modelspecific to each asset being monitored. Thousands of simulations areperformed using these models in order to make predictions as to wheneach piece of equipment will require maintenance and what type ofmaintenance is likely to be required. Alerts are also raised if anyobserved condition is deemed to necessitate immediate corrective action.Finally, the algorithm identifies patterns in the data collected, usingthis information to improve its models and communicating them to theuser so they may adapt their equipment management strategiesaccordingly. All alerts, maintenance recommendations, and reports arecommunicated to the user through a dashboard accessed via computer ormobile device.

The system is designed to meet the needs of the healthcare equipmentmanagement field (also called “clinical engineering”). Traditionally,clinical engineers perform preventative maintenance on medical equipmentat scheduled intervals following original equipment manufacturer (OEM)recommendations. This is inherently inefficient, as degradation resultsnot directly from the passage of time, but from equipment use andsuboptimal environmental conditions. Moreover, the desire to curtailliability and strengthen parts sales gives manufacturers the incentiveto recommend overly-frequent preventative maintenance tasks. The rapidproliferation of equipment in hospitals has amplified theseinefficiencies, making maintenance compliance prohibitively expensiveand compelling the key oversight groups, the Centers for Medicare andMedicaid Services (CMS) and The Joint Commission (TJC), to allowhospitals to enact maintenance policies that deviate from OEMrecommendations as long as patient safety is preserved. The freedom toutilize these customized policies, termed alternative equipmentmaintenance (AEM) programs, has thrust the industry into a state oftransformation, sending clinical engineers everywhere on a hunt for datathat will support the formation of reliable, sustainable AEM programs.This system satisfies that demand by gathering and analyzing the datamost relevant to the making of well-informed maintenance decisions. Therich insights into equipment health gleaned by the system will enableclinical engineers to abandon inefficient OEM-recommended maintenanceintervals in favor of dynamic, condition-based policies. This approachwill eliminate unproductive preventative maintenance tasks, generatinghundreds of thousands of dollars in savings for hospitals, whileinforming better inventory decisions and bolstering patient safety.

While it was designed with healthcare facilities in mind, the system canbe used in any equipment-intensive industry. Forensics laboratories,engineering shops, and factories will all benefit from the optimizationof asset management and maintenance that is enabled by this system. Infact, factories and processing plants across the globe have beenutilizing condition-based maintenance policies for years through theadoption of self-monitoring equipment. This is a technique pioneered bythe U.S. military which also has condition-monitoring technologyinstalled in its planes, ships, and equipment. In each of these cases,though, the monitoring system is specific to a single type of equipmentand contained within each machine. The technology described in thisdocument improves on this with a one-system-fits-all approach. The sameplug and tag modules may be deployed on any class or type of equipmentin any setting. They are interoperable and all data is handled by thesame adaptive algorithm. This means that, for example, data collectedwhile monitoring a drill press in a fabrication shop may later be usedto bolster the degradation forecast of a 3D printer installed in thatshop years later. Unlike current technology, the system extends beyondeach individual piece of equipment, forming a picture of the equipmentenvironment within the facility as a whole. This leads to much morepowerful, accurate insights which, again, may be used in anyequipment-intensive setting.

These and other objects of the invention, as well as many of theintended advantages thereof, will become more readily apparent whenreference is made to the following description, taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows an exemplary system in accordance with the preferredembodiment of the invention, outlining the data flow between subsystemsand users.

FIG. 2 shows an exemplary embodiment of the invention in which thedashboard and/or user interface shows system alerts and recommendations.

FIG. 3 shows an exemplary embodiment of the invention where a plug andan asset-monitoring module(s) is deployed on a generic asset.

FIG. 4 shows an exemplary embodiment of the invention in which thesystem is deployed in a facility.

FIG. 5 shows an exemplary embodiment of the invention in which the plugmodule is connected to the power cord of a generic asset.

FIG. 6 shows an exemplary embodiment of the invention in which data andpower flow in the plug module circuitry is diagrammed.

FIG. 7 shows an exemplary embodiment of the invention with a tag modulein a hard plastic casing.

FIG. 8 shows an exemplary embodiment of the invention in which data andpower flow in a tag module is diagrammed.

FIG. 9 shows an exemplary embodiment of the invention in whichproximity-based location tracking is performed with one beacon per roomand where the system uses altimeters to reduce the number of requiredbeacons.

FIG. 10 shows an exemplary embodiment of the invention in whichsub-algorithms and their interactions with each other and externalsubsystems are diagrammed.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In describing the illustrative, non-limiting preferred embodiments ofthe invention illustrated in the drawings, specific terminology will beresorted to for the sake of clarity. However, the invention is notintended to be limited to the specific terms so selected, and it is tobe understood that each specific term includes all technical equivalentsthat operate in similar manner to accomplish a similar purpose. Severalpreferred embodiments of the invention are described for illustrativepurposes, it being understood that the invention may be embodied inother forms not specifically shown in the drawings.

The asset-monitoring network is the data collection arm of the system.The foundation of the network consists of two modules (the plug moduleand tag module) designed to be affixed or connected to equipment. Oncedeployed, these asset-monitoring modules form a wireless network andbegin transmitting collected data. In certain embodiments, the networkis a mesh network. In contrast to other configurations such as star andtree networks, a mesh network establishes data routing in a dynamic,adhoc manner, making this topology more efficient and resilient thanalternatives.

FIG. 1 is an exemplary system 100 in accordance with the preferredembodiment of the invention, outlining the data flow between subsystemsand users. The Remote Data Analysis Algorithm 102 is responsible forcontrolling the logic of the system. The Remote Data Analysis Algorithm102 may operate on processing device 101, such as a computer or server.The processing device 101 can be centrally located and communicate withone or more data servers 114, such as through the internet or a globalor local network. For example, the central processing device 101 can bea server or the like that is located at a building, and the data servers114 can be on different floors of the building. Still further, the dataservers 114 can each be provided in different buildings, and the centralprocessing device 101 can be remotely located and communicate with eachof the data servers 114. In yet another embodiment, the data processor101 can be co-located and/or integral with one or more of the dataservers 114.

The Remote Data Analysis Algorithm 102 acts to collect and output dataincluding equipment location and alerts 104 as well as status reports,alerts, and maintenance recommendations 106. Alerts can be raised, forexample, when asset conditions are detected that violate user-definedrules or are known to cause harm to assets. Examples of conditions thatmight actuate an alert include: temperature or humidity outside of amanufacturer-specified operating range, asset relocation to a restrictedarea, physical abuse of asset, asset exposure to a power surge, andasset overuse. Other output data such as status reports and maintenancerecommendations may be issued dynamically as observed conditions changeand generate new information, periodically (e.g., daily, weekly), or ondemand. Still further, alerts can be generated due to a change in asensed condition such as current, altitude, movement.

These data outputs are selectively provided to either operators 108and/or managers 110, each of who receive that data on a networkeddevice, such as a computer, tablet, or smartphone. Determination of dataprovisioning and calculation of that data is performed by the RemoteData Analysis Algorithm 102 based its analysis of aggregated usage andcondition data 112. This data is aggregated from one or more dataservers 114 a, 114 b, 114 c that are networked to the computer runningthe Remote Data Analysis Algorithm 102. The data servers 114 a, 114 b,114 c are responsible for collecting equipment-level sensor reading 116from asset-monitoring networks 118 a, 118 b, 118 c and forwarding thatdata upstream to the Remote Data Analysis Algorithm 102.

FIG. 2 shows an exemplary embodiment of the invention in which thedashboard and/or user interface 200 displays system alerts andrecommendations to operators 108 and/or managers 110. The user interface200 can be provided at the processing device 101, and/or at one or moreof the data servers 114, or through a networked device such as acomputer, tablet, or smartphone. The user interface 200 may take theform of an “Equipment Management Dashboard,” as shown in FIG. 2. Theuser interface 200 displays real-time data related to equipment (assets)that the system 100 is designed to oversee. In this exemplaryembodiment, the user interface 200 allows the operators 108 and/ormanagers 110 to select a device/asset to display 202, the location ofthat device/asset 204, the status of the device/asset 206, utilizationof that device/asset 208, the appearance of the device/asset 210, andalerts 212 associated with that device/asset. Using the user interface200, operators 108 and/or managers 110 are able to actively monitor andtrack devices/assets that are networked to the system. Operators 108and/or managers 110 are also able to use the user interface 200 to findassets 300 that are being tracked by selecting the “LOCATE” button 214.

FIG. 3 shows an exemplary embodiment of the invention where anasset-monitoring module is deployed on a generic device/asset 300. Theasset-monitoring module can be, for example, in the form of a plugadapter 302 or a tag module 304. In one embodiment of the invention, theplug module 302 is designed to mate with standard male electrical plugs(A/C as well as D/C) and is networked to the system 100. In this manner,the system can monitor activity and usage of the device/asset 300. Thetag module 304 is similarly associated with the device/asset 300, suchas by being affixed to the housing of the asset 300 (by an adhesive,fastener or the like), and is also networked to the system 100. Asexplained further below, the asset-monitoring module 304 providesadditional data on the performance of the device/asset 300 for analysisby the system. In one embodiment, each asset-monitoring module 302, 304is associated with a single asset 300 and each asset 300 has one or moreasset-monitoring modules 302 and/or 304. The tag module 304 does notrequire the asset to have an AC plug in order to operate, as it maystill monitor environmental and operational conditions in its absence.

FIG. 4 shows an exemplary embodiment of the invention in which thesystem 100 is deployed in a facility. For example, a facility may be ahealthcare facility such as a hospital, office, domestic residence, carecenter, medical building or other location. In FIG. 4, this facility hasthree patient rooms 402 a, 402 b, 402 c, a staff station room or area404, and a storage room or area 406. In this exemplary implementation ofthe system, one or more asset-monitoring modules 408 are deployedthrough the three rooms 402 a, 402 b, 402 c; a staff station 404, andstorage 406. Asset-monitoring modules 408 may be, for example, a plugmodule (such as plug 302) or a tag module (such as tag module 304), andare each associated with a respective asset 300 (not shown in FIG. 4 forease of illustration).

The asset-monitoring modules 408 are equipped with one or more sensors,enabling the observation of environmental conditions such astemperature, humidity, atmospheric pressure, vibrations and movement,magnetic field strength, radiation, etc. Each type of module 408 alsohas unique data-collection specialties, with the tag module 304 alsomonitoring operational conditions (e.g., if equipment is dropped orshaken), and the plug module 304 monitoring equipment usage (e.g. howmany hours equipment has been in use, frequent modes of operation).Environmental conditions, operational conditions, and usage informationare all major factors in the performance, longevity, and maintenancerequirements of equipment. By monitoring them closely, the system isable to estimate the health of the equipment in real-time, raisingalerts and making recommendations accordingly.

The asset-monitoring modules 408 are networked together by wirelessconnection 412. This wireless connection 412 forms a network that isalso able to determine the location of equipment within the facility byusing specialized beacon modules 410. This is helpful for users tolocate a specific asset 300 within a large facility, and is alsoimportant for the optimization of equipment management plans. Forexample, the system 100 may detect that a certain room is too humid,that equipment is stored far away from where it is needed, or thatequipment used in a certain area of the facility is treated more harshlythan in other areas. As demonstrated by these examples, observations onequipment conditions may be more insightful with the addition oflocation information.

All modules 408, 410 in the network are capable of dual-bandcommunication, transmitting and receiving information using bothhigh-frequency (e.g., 2.4 GHz) and low-frequency (e.g., 915 MHz) bands.The high-frequency signals are sharply attenuated by obstacles such aswalls and ceilings, making them ideal for location tracking (asexplained later, with respect to the beacon modules 410) while thelow-frequency signals are not attenuated as much by obstacles, makingthem suitable for data transfer by modules 408, 410 across largefacilities. As shown, one or more local beacons 410 a can be placed tobe in wireless communication with one or more asset-monitoring modules408. Communication of the system is mediated by the data server 114 a.

For instance, the beacon(s) 410 a can be located in a same room 402, 406as the asset-monitoring modules 408. In addition, one or morecentralized beacons 410 b can be placed to be in wireless communicationwith the local or other central beacons 410 a, 410 b. The local beacons410 a receive data signals from the asset-monitoring modules 408 andconvey them directly or via one or more central beacons 410 b to thedata server 114. As further shown, the asset-monitoring modules 408 cancommunicate with a local beacon 410 a positioned in another room, or cancommunicate directly to a central beacon 410 b. The central beacons 410b can be positioned in a more central location (such as a hallway,lobby, or waiting area), to link the asset-monitoring module 408 and/orlocal beacons 410 a to the data server 114 a.

Plug Module 302

Turning to FIG. 5, details of the plug asset-monitoring module 502 areshown, which can be for example the plug module 302 shown in FIG. 3. Theplug module 502 has a body or housing 504, male electrical member 510and female electrical member 512. The body 504 can generally have a cubeor be elongated to have a rectangular shape and is approximately thesame size as an AC plug 305 of the asset 300, as shown, though anysuitable size and shape can be utilized. The body 504 has a proximal end506 and an opposite distal end 508, and can be for example solid moldedplastic casing. The male electrical member 510 can be a standard 2- or3-pronged plug (with a positive line and a negative and/or ground line)and is positioned at the distal end 508 and the female electrical member512 can be a standard 2- or 3-socket receptacle (with a positive lineand a negative and/or ground line) and is positioned at the proximal end506.

The plug adaptor or module 502 acts as an intermediary between the ACplug 305 of the electronic asset 300 and a power source, such as an ACoutlet. The power cord 305 of the asset 300 has a male plug that isremovably engaged and received by the female electrical member 512 atthe proximal end 506 of the plug body 504. And the male electricalmember 510 is inserted into the female receptacle of the AC wall socketoutlet. In so doing, an electrical connection is established from theasset plug 305, from the female electrical member 512 to the maleelectrical member 510, to the AC wall outlet, so that the asset 300draws the needed AC power from the wall outlet as if the asset 300 wereplugged directly into the wall outlet.

A current sensor 618 (FIG. 6) is positioned inside the plug body 502,between and electrically connected with the female electrical members512 and the male electrical members 508. Accordingly, an electricalconnection is established from the female socket 512 to the currentsensor 618, and from the sensor 618 to the male prongs 510. One of thecurrent-carrying lines (e.g., the positive socket and plug of themembers 512, 510 or the negative socket and plug of the members 512,510) passes through the current sensor 618, though the current sensor618 can be connected to the male and female members 510, 512 in anysuitable manner.

The sensor monitors the amount of electrical current drawn by themachine or asset 300 at any given time, and can therefore discernwhether the asset 300 is on or off, as well as the mode in which themachine is being operated (e.g., high/medium/low; heating/cooling). Uponmodule installation, the user is instructed to turn the asset 300 on,then off, then on again, and to cycle through its different operationalmodes. The plug module 502 observes and registers the current drawn asthe machine transitions between each mode, using this information tointerpret future observations. A user enters the mode into the userinterface 200 as he goes through each mode. For example, when a userturns off an asset, he may enter “OFF” into the user interface 200 sothat the system 100 can associate that current level with the “OFF”mode. In an alternative embodiment, the characteristic current draw ofeach mode could be provided by the manufacturer and manually orautomatically entered into the system 100. All data collected by theplug module 502 is transmitted wirelessly to data servers 114 a, 114 b,114 c. These data servers 114 a, 114 b, 114 c could be on differentfloors, different ends of the same floor, or different facilitiesaltogether. Moreover, there could be hundreds of data servers, eachserving a separate asset-monitoring network and all feeding data intothe same Remote Data Analysis Algorithm 102.

FIG. 6 shows an exemplary embodiment of the invention in which data andpower flow in the plug module 502 circuitry is diagrammed. As shown,data flows within the circuitry are shown as the dashed line 630,internal power is shown by the dotted line 632, and the main power isshown as the solid line 634. The plug module is controlled by aprocessing device 602 such as a microprocessor. The processing device602 receives and transmits data using a data connection 630. Themicroprocessor 602 is connected to memory 620 that may store datarelated to operation of the module 502, such as asset identification,asset location, and the operational mode information. The memory 620also stores monitoring data received from the module 502 such as theamount of electrical current drawn by the machine at any given time,whether the asset status (e.g., whether the machine is on or off), aswell as the operational mode in which the machine is being operated(e.g., high/medium/low; heating/cooling).

The microprocessor 602 is also connected to an RFID reader circuit 604,and matching circuits 608 a, 608 b. The RFID reader circuit 604 isresponsible for receiving and transmitting data through an antenna 606(e.g., at 13.68 Mhz) and allows the plug module 502 to receive andtransmit data about the asset 300. The matching circuits 608 a, 608 bimplement dual-band communication, transmitting and receivinginformation using both a high-frequency (e.g., 2.4 GHz) antenna 610 anda low-frequency (e.g., 915 MHz) antenna 614, respectively. Antenna 606receives such data as the asset ID by interrogating the RFID tag moduledescribed in detail below. Antenna 610 communicates with beacon module410 which is a method by which asset location can be determined. Antenna614 may also be used for the transmitting and receiving of collecteddata and instructions between the asset-monitoring modules 502 and 700and the data server 114 a. This data transfer (occurring via antenna614) may also have a beacon module 410 as an intermediary.

The microprocessor 602 receives sensor data related to the asset fromone or more environmental sensors 612, such as thermometers,hygrometers, and altimeters. This is among the data transmitted throughthe antennas. Specifically, this is data transmitted through antenna614. So sensors 612 take readings (e.g., X degrees Celsius, Y % relativehumidity, Z Pa pressure) which are received by microprocessor 602, andthis data is then transmitted to data server 114 via 608 b, 614. Thisinformation is then used by Remote Data Processing Algorithm 102 asshown in FIG. 10. The data collected from the current sensor 618 mayalso be transmitted through the antenna 614 so that the system is awareof the power consumption and usage statistics of the asset 300.

The power supply 616 to the circuitry is transferred through the AC/DCconvertor 622, which preferably transmits 5 V to the charging circuit624. The charging circuit 624 converts that 5 V to 4.2 V so that it cancharge the battery 626. The battery 626 may be lithium-ion or any otherrechargeable type known in the art. The battery 626 preferably outputs3.7 V to the voltage regulator 628. The voltage regulator 628 thenoutputs 3.3 V to the various components of the circuitry, including thesensors 612, 618, RFID reader circuit 604, microcontroller 602, matchingcircuits 608, and memory 620. The figure also illustrates that the inputpower 616 is received from the wall socket via the male prongs 510. Inaddition, that input power supply 616 proceeds through as the outputpower supply 617 at the female members 512 to provide a power supply tothe asset plug 305.

Tag Module 304

Referring to FIG. 7, the second asset-monitoring device is the tagmodule 700 (such as tag 304), which is a small, lightweight, wirelessdevice that is affixed to equipment via adhesive or suction cup. Once inplace, the tag module (similarly to the plug module) uses an array ofsensors to observe environmental conditions. Using an internalaccelerometer and gyroscope, the tag can detect when equipment isdropped or shaken, as well as the severity of such events. This data istransmitted wirelessly to a central server in the same fashion as thedata collected by the plug module.

The tag module 700 may include a user-programmable LCD or e-paper screen702, allowing it to display information relevant to the asset 300 beingmonitored such as make/model, date of last inspection, owner, and othersuch data. These functionalities may be achieved through a softwareapplication on a networked device such as a smartphone, tablet, orcomputer. For example, it could be achieved through dashboard 200. Thedashboard 200 may also be used to display status and mode informationrelated to the asset 300. The tag module is preferably enclosed in ahard plastic housing or casing 704.

FIG. 8 shows an exemplary embodiment of the invention in which data andpower flow in a tag module 700 is diagrammed. As shown, data flowswithin the circuitry are shown as the dashed line 830 and internal poweris shown by the dotted line 832.

As with the plug module (FIG. 6), the tag module is controlled by aprocessing device 806 such as a microprocessor that receives andtransmits data using data connection 830. The microprocessor isconnected to memory 820 that stores data related to operation of themodule as well as data monitored by the module such as the amount ofelectrical current drawn by the machine at any given time, whether themachine is on or off, as well as the mode in which the machine is beingoperated (e.g., high/medium/low; heating/cooling).

The microprocessor 806 is also connected to an RFID reader circuit 804,and matching circuits 810 a, 810 b. The RFID reader circuit 804 isresponsible for receiving and transmitting data through a (e.g., 13.68Mhz) antenna 802 and allows the tag module to provide data about theasset 300. The matching circuits 810 a, 810 b are responsible forimplementing dual-band communication, transmitting and receivinginformation using both high-frequency (e.g., 2.4 GHz) antenna 812 and alow-frequency (e.g., 915 MHz) antenna 814, respectively.

The microprocessor 806 receives sensor data related to the asset fromone or more environmental sensors 816, such as thermometers,hygrometers, and altimeters. This is among the data transmitted throughthe antennas. Specifically, this is data transmitted through antenna814. Environmental sensors 816 take readings, which are received bymicroprocessor 806, and this data is then transmitted to data server 114via matching circuit 810 b and antenna 814. This information is thenused by Remote Data Processing Algorithm 102 as shown in FIG. 10. Themicroprocessor 806 is also connected to one or more operational sensors818, such as an accelerometer or gyroscope. The data collected from theoperational sensors 818 may also be transmitted through the antenna.Operational sensors 818 take readings, which are received bymicroprocessor 806, and this data is then transmitted to data server 114via matching circuit 810 b and antenna 814. This information is thenused by Remote Data Processing Algorithm 102 as shown in FIG. 10.

The power supply to the circuitry is transferred through the powersource 822, preferably using DC power, such as an inductive charger, USBconnection, or AC/DC converter. In one embodiment of the invention, thepower source 822 transmits 5 V to the charging circuit 824. The chargingcircuit 824 converts that 5 V to 4.2 V so that it can charge the battery826. The battery 826 may be lithium-ion or any other rechargeable typeknown in the art. The battery 826 preferably outputs 3.7 V to thevoltage regulator 828. The voltage regulator 828 then outputs 3.3 V tothe various components of the circuitry.

Beacon Module 410

Referring to FIGS. 4 and 9, the beacon module 410 may be battery ormains powered and is deployed throughout a facility, remainingstationary wherever they are placed. The beacon modules 410 broadcasthigh-frequency locator signals which are sharply attenuated by obstaclessuch as walls and ceilings. An asset-monitoring module 408 detects thesesignals and can discern from the observed signal strength which beacon410 it is closest to. Because the location of a beacon does not change,this is a means by which equipment location within a facility may bedetermined. A beacon module 410 does have an antenna, housing, andprocessing device, but it does not have sensors. One example of locationtracking works as follows. The asset-monitoring module 408 emits ahigh-frequency signal indicating that its location needs to bedetermined. This prompts any beacon module 410 which receives thissignal to reply, broadcasting the signal with a unique beacon ID. Theasset-monitoring module 408 receives these replies and determines whichbeacon module 410 it is nearest to, based on which reply has thegreatest signal strength. The asset-monitoring module 408 thencommunicates this information to data server 114, which “knows” whichbeacon ID is associated with which room or location. This informationwould have been stored, for example, on the data server 114 duringinstallation of the system. An alternative embodiment is for each beaconmodule 410 to periodically broadcast its unique beacon ID, rather thanbeing prompted by the asset-monitoring module 408.

Beacon modules also serve as signal repeaters and amplifiers within thenetwork. If an asset-monitoring module transmits data to a beaconmodule, the beacon module will then echo this transmission (typicallytransmitting with more power than the originator of the data),effectively increasing the range at which asset-monitoring modules cancommunicate. This helps to maintain the link between asset-monitoringmodules and the central server, even as these modules move across afacility or campus.

The plug and tag asset-monitoring modules 302, 304 have additionalcommon features beyond the environmental condition monitoring describedearlier. One such feature is the inclusion of an altimeter, a sensorwhich observes atmospheric pressure and translates this into somealtitude with respect to sea level. This allows the modules to recognizewhat floor of a facility they are on, thus enabling locator signals froma beacon module to be used not only by modules on that floor, but alsothe floors immediately above and below. For example, if two modules eachdetect that the locator beacon in room 401 is nearest, but one modulereports an altitude ten feet less than the other, then the system isable to determine that one asset is in room 401 and the other is onefloor below in room 301. This same principle may be applied to equipmentin room 501 (not shown). Thus, a single locator beacon may be usedacross three vertically distributed rooms, allowing for a sharpreduction in wireless infrastructure.

There typically will be one beacon module 410 per room and one to twoasset-monitoring module 408 per asset. Several assets, and thereforeseveral asset-monitoring modules 408, can be in a room at once. There isno functional difference between the rooms 402 a, 402 b, 402 c, thestaff station 404, and the storage room 406 as far as the system isconcerned as these are all just rooms. You may have one asset-monitoringmodule 408 as in rooms 402 a, 402 b, 402 c, no asset-monitoring module408 as in staff station 404, or several asset-monitoring modules 408 asin storage room 406. Multiple beacon modules 410 may be located in aroom if it is large, such as in 404. This could be the case if one isusing beacon modules 410 as signal repeaters as described above andneeds to ensure that data can be transmitted down a long hallway oraround a corner. It could also be the case if the room is sufficientlylarge (such as a warehouse or emergency room) such that it is not enoughto know that an asset is in the “room” but instead it must be known whatbay or wing it is in.

There is one data server 114 per asset-monitoring network, but many suchnetworks may exist within the system. For example, a hospital may wishto set up one small network in the ER and one separate network in thePACU as these are the only units where they care to monitor assets andthe units (in this particular hospital) are located far apart. Eachnetwork will have its own data server 114 a. A different hospital maywish to monitor assets all throughout the facility and so they install anetwork that spans the whole building. While this network is much largerthan either of those implemented in the first hospital, it only requiresone data server 114 a. Asset-monitoring modules 408 are affixed toassets and therefore are positioned wherever the assets are moved to.Beacon modules 410 are placed where they can service a room and areunlikely to interfere with other beacon modules 410 and confuse thesystem (so you would not install two beacon modules 410 on either sideof a single wall. Beacon modules 410 may also be placed at strategiclocations like either end of a long hallway in order to establish acommunication link between disparate parts of the facility. Theplacement of the data server 114 a is unimportant as long as it islocated where it can reliably communicate with the rest of theasset-monitoring network.

FIG. 9 shows an exemplary embodiment of the invention in whichproximity-based location tracking is performed with one beacon moduleper room and where the system uses altimeters to reduce the number ofrequired beacons. In this exemplary implementation, the system isimplemented in a three-story facility with six rooms. The system isoptimized to deploy as few modules as possible to spatially locate eachasset in the facility. FIG. 9 shows previously identifiedasset-monitoring modules 408, beacon modules 410, and a wirelessconnection 412 deployed across the facility. The facility is comprisedof a plurality of rooms, with one or more assets in each of the rooms.

Rooms 301, 302, 201, 202, 101, and 102 (902 a, 902 b, 902 c, 902 d, 902e, 902 f) are shown in FIG. 9. Some of these rooms may have multipleassets situated in them, while others may not have any. Each asset istagged with an asset-monitoring module 408 that incorporates analtimeter. The altimeter may be used to determine the elevation of theasset, and that elevation may be correlated to a determination of thefloor on which the asset is located. As explained above, the altimetermay be measured with respect to the location of a single beacon module410.

In this manner, as shown in rooms 302, 202, and 102 (902 b, 902 d, 902f), the single beacon module 410 in room 202 (902 d) is used todetermine the location of assets located on all three floors, becausethe single beacon module 410 acts as a reference point for thealtimeters of the asset-monitoring modules 408 on each floor. In theexemplary FIG. 9, the system first uses the asset-monitoring module 408paired with the beacon module 410 in room 202 to determine that theasset is located in room 202. On that basis, because theasset-monitoring module 408 in room 102 (902 f) is located ten feetbelow the beacon module 410, already known to be in room 202 (902 d),the system calculates that the asset in question is located in room 102(9021). On that same basis, the system can determine that theasset-monitoring module 408 located ten feet above the beacon module 410is located in room 302 (902 b).

Another common feature is the ability of the asset-monitoring modules408 to detect and read small RFID tags, utilizing the RFID readers 604,804. The user has the option to install a unique RFID tag on each asset300 to be monitored. The asset-monitoring module 408 reads the RFID tagand instantly knows which specific piece of equipment is beingmonitored. That is particularly useful when a module 408 is replaced(due to module failure, low battery, etc.), as the replacement modulewill automatically identify the asset 300 being monitored.

The asset ID is stored in the RFID tag itself. When the module 408 isaffixed to the equipment, it is to be placed overtop or adjacent to thisRFID tag. Once in place, the RFID readers 604, 804 will interrogate theRFID tag via antennas 606, 802. “Interrogate” in this context means thatthe reader 604, 804 emits radio waves, causing the RFID tag to respondwith the asset ID which it has stored. Once extracted, this informationis stored within the microcontroller 602, 806. The system couldalternatively be configured either so that whenever module 408 transmitsdata collected to the data server 114 a, it includes the asset ID in themessage, thus associating said data with asset 300; or it may beconfigured so that upon interrogating the RFID tag, the module 408signals to the data server 114 a that it is now associated with theasset 300 and any data later transmitted by module 408 (i.e., carryingthe asset-monitoring module ID) is to be associated with the asset 300.This ensures that there is no gap in asset monitoring due to modulereplacement, and modules may be shifted from asset to asset as neededwithout creating errors or confusing the system's analysis.

Data Analysis

The system may also apply a data analysis algorithm which works tointerpret the data collected by the asset-monitoring network (consistingof plug and tag modules), and leveraging it to inform equipmentmanagement strategies. This algorithm consists of five uniquesub-algorithms which are described in FIG. 10.

FIG. 10 shows an exemplary embodiment of the invention in whichsub-algorithms and their interactions with each other and externalsubsystems are diagrammed. Together, these sub-algorithms comprise thedata processing algorithm 1000 (also shown as Remote Data AnalysisAlgorithm 102 in FIG. 1). The data processing algorithm 1000 (and itssub-algorithms) receive equipment/asset performance and defect reportsfrom external databases 1002, which are incorporated by the system intoits calculations.

The first module of the data processing algorithm 1000 is the modelgeneration sub-algorithm 1004, which is invoked when a new piece ofequipment is brought into the asset-monitoring network 1018. Thissub-algorithm takes as input from the user via the user interface 1006(such as via the user interface 200 for the computer 101 or server 114)including general equipment information such as class, make, and model,and uses this to search external databases 1002 (populated by equipmentmanufacturers, or groups like the Emergency Care Research Institute orthe U.S. Food and Drug Administration) for relevant performance data andknown deficiencies or defects. The user via the user interface 1006 isalso asked to input information specific to the asset 300, such asmaintenance history and estimated prior use. Using these factors, amathematical model of degradation is formed specific to that device. Themodel is then associated with the specific asset ID. This information isprovided by the user through some networked device such as a computer,tablet, or smartphone and is then communicated to the Remote DataAnalysis Algorithm 102.

This model includes an initial estimate of equipment capacity (which maybe defined in percentage terms or number of operational hours remainingbefore maintenance is required). This is information pulled from outsidesources (the database) or, if no information is available, a rough guessis made such as “a lightly used asset has an initial capacity of 90%” or“a heavily used asset has an initial capacity of 75%” These assumptionsmay be improved through pattern recognition over time, as well as theexpected sensitivity of the equipment to those conditions observed bythe asset-monitoring network (e.g., temperature, humidity, vibration,usage). These sensitivity coefficients are ascertained from pastobservations, for example from external databases 1002 and from datathat the user has input at the user interface 1006. As the systemobserves patterns and updates its assumptions, future models becomestronger as they start off with better information. This modeling needsto be dynamic, as different types of equipment have different tolerancesto environmental and operational conditions, and their internalcomponents wear at different rates. The ability to form a uniquedegradation model for each type and piece of equipment is integral tothe system and its ability to incorporate a wide variety of assets.

The central component of the degradation model is the mathematicalexpression of instantaneous degradation, which is a measure of assetcapacity lost at each moment in time. Instantaneous degradation isexpressed as a summation of some number of degradation constituents,with each constituent representing a unique contributing factor to assetdegradation. There are two classes of constituent: progressive-based andshock-based. Progressive-based constituents represent processes orconditions that cause incremental, continuous degradation over time suchas humidity, asset use, low-level vibrations, and even the passage oftime itself. Each progressive-based constituent has an underlyingstatistical distribution (reflecting the stochastic nature of thesedegradation processes) which is then multiplied by the magnitude of theobserved condition and a coefficient representing the asset sensitivityto this condition. For example, if an asset has a humidity sensitivitysuch that asset capacity is reduced by 0.005% for every 10% increase inrelative humidity, then such an asset stored in a room with 60% relativehumidity will have a degradation due to humidity equaling 0.03% persecond. Each progressive-based degradation constituent shares this form.

Shock-based degradation constituents represent occasional, traumaticevents that may occur randomly, have random severity (i.e. magnitude),or both. Examples of shock-based processes include temperatureover/under a critical level, power surges, and drops. Each shock-basedconstituent has one or two underlying statistical distributions(depending on whether magnitude and/or time of occurrence are random)which is multiplied by a coefficient representing the asset sensitivityto this condition. For example, if an asset has its capacity reduced by5% for every pound of force undergone when dropped, and when a droprandomly occurs a 12 pound impact is experienced, the asset degradationdue to the drop will equal 60%. It should be noted that conditionsensitivity is not always linear as described here for humidity anddropping, but the overall structure of the model remains the same, evenwith more complex relationships.

This model is then used by the degradation tracking sub-algorithm 1008to interpret incoming data collected by the asset-monitoring network1018 and therefore determine real-time equipment health (i.e., remainingcapacity). The asset will also have a sensitivity to humidity, usage,the passage of time, and any other environmental/operational conditionstracked by the system. For example, if the model generated for aspecific defibrillator stipulates that being dropped from waist heightwill reduce asset capacity by 20% and the tag module affixed to thedefibrillator has registered two drops of this magnitude, for examplethrough the accelerometer, then the degradation tracking sub-algorithmwill deduce that the capacity of the defibrillator has been reduced by40%. In practice, there are dozens of conditions being trackedsimultaneously in real-time. Furthermore, condition sensitivity willfrequently be non-linear (e.g., a temperature increase from 300 degreesF. to 370 degrees F. will do more damage to an asset than an increasefrom 20 degrees F. to 90 degrees F. even though these temperaturechanges have identical magnitude) and the degradation caused by onecondition may be magnified by another condition. All of thesecomplexities are captured in the model generated by the model generationsub-algorithm 1004 and their real impacts are tracked by the degradationtracking sub-algorithm 1008.

Whenever the estimate of current equipment health is updated, thisinformation is used by the simulation sub-algorithm 1010 to forecastfuture degradation in a dynamic intertemporal forecasting heuristic.This sub-algorithm draws on a wide variety of metrics (e.g., present andhistorical rates of degradation, seasonal shifts in equipment usage,trends in the ambient environment) as it simulates thousands of possibledegradation paths that may be taken. In so doing, the sub-algorithmdetermines which outcomes are unlikely (e.g., the infusion pump willundergo a catastrophic failure this week) and which are very likely(e.g., the infusion pump will require replacement parts in February dueto increase usage during “flu season”). The intertemporal structure ofthis sub-algorithm ensures that each time new data is collected by theasset-monitoring network, the inferred health of the equipment health isrecalculated, new simulations are performed, and the forecast isupdated.

Degradation simulations are performed by using the intrinsic capabilityof a computer to mimic statistical distributions using random numbergenerators. For each simulated time step, the algorithm considers eachindividual degradation constituent and generates a random value derivedfrom the same statistical distribution associated with that constituent.Once all variables have been assigned a value, the expression forinstantaneous degradation is solvable and a simulated degradation valueis determined. This value is subtracted from the estimated current assetcapacity, yielding the simulated asset capacity for the given time step.This process is repeated indefinitely until the simulated asset capacityreaches zero (or some user-defined minimum acceptable capacity). Thetime step at which this occurs is recorded, and then a new simulation isconducted. The algorithm performs thousands of these simulations, eachtime recording the time at which the simulated capacity reaches zero(i.e., the time at which the simulated asset needs maintenance). Theforecast is the reported distribution of these simulated maintenancetimes, as well as key statistics such as the mean and standard deviationof this distribution.

The action sub-algorithm 1012 converts this degradation forecast intoactionable information for the end-user. Based on a set of preferencesdefined by the user and the likely degradation path of the equipment,the sub-algorithm will make maintenance recommendations. The main rulesbeing followed are: 1) if the degradation-tracking sub-algorithm 1008finds that enough degradation has occurred such that asset capacity isnow at zero (or beneath some user-defined minimum acceptable capacity),immediate maintenance is required; and 2) If the forecast generated bysimulation algorithm 1010 is significantly altered by some condition,the algorithm must notify the user of that condition and include theappropriate pre-defined maintenance recommendation. Some examples are:(a) The defibrillator has been dropped 3 times. Check the defibrillatorfor damage. (b) The refrigerator is drawing 500 mA more current thanusual. Fan cleaning or replacement is recommended. (c) The ultrasoundmachine has seen 2.3 times more use this week than last. Scheduleinspection to be one week earlier than planned. (d) The vitals monitorhas now been used for 5,000 hours. Perform standard maintenanceprocedure within the next 10 days. (e) High humidity detected around 3Dprinter. Perform corrosion check during next inspection. (f) Infusionpump has entered cleaning room 50 times. Check for chemical damageduring next inspection.

This sub-algorithm may also access raw, real-time data collected by theasset-monitoring network 1018 in order to alert the user to conditionsrequiring immediate attention. These are pre-defined “If, then”relationships. For example, “If humidity is higher than X, do Y.” Theconditions are initially provided by the user or taken from an outsidedatabase. Such a condition could be ambient humidity above the tolerancespecified by the manufacturer, the movement of an asset to a restrictedlocation, overuse, or any other user-specified condition. Other examplesinclude: (a) Centrifuge is vibrating at a lower frequency than usual.Emergency parts replacement is recommended. (b) High temperaturedetected around dialysis machine. Cooling fan replacement may berequired. (c) Power surge detected by ventilator. Inspect for circuitdamage. (d) Impact detected. Inspect for case for damage.

The final module in the data processing subsystem is the patternrecognition sub-algorithm 1016. This sub-algorithm uses statisticalmethods such as regression analysis to detect relationships between ahost of variables including but not limited to:operational/environmental conditions, equipment location, maintenancefrequency, alerts and types of alerts, equipment failures,class/make/model of equipment, equipment usage modes and levels, andtime of day/month/year. By uncovering subtle patterns that affectequipment performance, this module is able to perform a process known asBayesian updating, whereby it uses results of its analysis and theinformation gained in the process to update the parameters of othersub-modules.

A typical Bayesian update would have the following steps: First, thepattern recognition sub-algorithm 1016 performs a regression analysis toascertain degradation relationships, uncovering those relationships thatwere previously unknown and using fresh data to arrive at betterestimates of the relationships which has been included in the model.Second, the pattern recognition sub-algorithm 1016 replaces the modelfrom model generation sub-algorithm 1004 with a newer version containingthese updated degradation constituents, sensitivity coefficients, andupdated statistical distributions. These steps repeats as thesub-algorithm 1016 receives additional data.

The continuous dynamic active learning due to the Bayesian updatedprocess allows the system to be corrective, adapting the assumptions(e.g., the statistical distribution of component malfunctions, the rateof degradation under typical ambient conditions) inherent to its modelsand forecast. This means that the longer the system is used, the moreaccurate the model generation, simulation, and action sub-algorithms1004, 1010, 1012 will become. It also means that the system can adapt toa wide variety of equipment, facilities, and user needs. Beyondleveraging these patterns for internal improvement, this sub-algorithmalso reports its findings to the user, empowering them to adapt theirown behavior and procedures accordingly.

In other implementations, the system may be built directly into andintegral with the asset 300. So instead of installing plug and tagmodules through a facility, the equipment itself is able to monitor allof these conditions and can interface wirelessly with the data analysisalgorithm. For example, a hospital may at some point in the future ordera GE vitals monitor, a Philips infusion pump, and a Siemens portableX-ray machine, and all of these different devices will be on theasset-monitoring network installed in the hospital. In this scenario, itis possible that these manufacturers purchase the hardware to install intheir equipment (as in the case of Intel processors), or it is possiblethat this system is instead expressed as a set of standards for themanufacturers to follow (as in the case of Bluetooth).

The system and method of the present invention is implemented bycomputer software that permits the accessing of data from an electronicinformation source. The software and the information in accordance withthe invention may be within a single, free-standing computer or it maybe in a central computer networked to a group of other computers orother electronic devices. The information may be stored on a computerhard drive, on a CD ROM disk or on any other appropriate data storagedevice.

The system and method of the present invention include operation by oneor more processing devices, including the computer 101, plugmicrocontroller 602, and tag microcontroller 806. It is noted that theprocessing device can be any suitable device, such as a computer,server, mainframe, processor, microprocessor, PC, tablet, smartphone, orthe like. The processing devices can be used in combination with othersuitable components, such as a display device (monitor, LED screen,digital screen, etc.), memory or storage device, input device(touchscreen, keyboard, pointing device such as a mouse), wirelessmodule (for RF, Bluetooth, infrared, WiFi, etc.), some of which arediscussed above and shown in FIGS. 6, 8. The information may be storedon a computer hard drive, on a CD ROM disk or on any other appropriatedata storage device, which can be located at or in communication withthe processing device. The entire process is conducted automatically bythe processing device, and without any manual interaction. Accordingly,unless indicated otherwise the process can occur substantially inreal-time without any delays or manual action.

A typical exemplary operation of the system follows. Sensor readings aremade by each asset-monitoring module 408 and then transmitted viaantennas 614, 814 to the data server 114 a using low-frequency (e.g. 915MHz) radio communication. If the asset-monitoring module 408 is out ofcommunication range of the data server 114 a, beacon modules 410 canserve as intermediaries, receiving this data from the asset-monitoringmodule 408 and transmitting identical data to the data server 114 a ifpossible, or to another beacon module 410. This may repeat severaltimes, with data transferring between several beacon modules 410 beforefinally reaching the data server 114 a. Beacon modules 410 alsocommunicate with asset-monitoring modules 408 via antennas 610, 812using high-frequency (e.g. 2.4 GHz) radio waves so that anyasset-monitoring module 408 may ascertain its location.

This location information, once determined, is also transmitted to thedata server 114 a over the low-frequency radio band. Eachasset-monitoring module 408, asset 300, and beacon module 410 have aunique ID. When the asset-monitoring module 408 is deployed, its beaconID becomes associated with the relevant asset ID, such as by using theRFID reader circuit 604 to receive the asset ID. If RFID chips are notused, then the user can manually associate the asset-monitoring moduleID with asset 300. Only the beacon ID is associated with certainlocation (e.g., building/room/floor/hall). Information received by thedata server 114 a is stored on the data server 114 a, then transmittedto the processing device 101 and the Remote Data Analysis Algorithm 102,such as through an internet connection. In this way, the data server 114a serves as the device on which the action sub-algorithm 1012 in theData Processing Algorithm 1000 (1000 itself being synonymous with 102)operates. Outputs from the Data Processing Algorithm 1000 arecommunicated to the user via the user interface 1006 or, for example,outputs 104, 106 are transmitted to users 108, 110 over an internetconnection for example, and displayed using the user interface 200 (FIG.2).

At the user interface 200 (FIG. 2), the user selects an asset using thedropdown menu 202, and then the user interface is populated withreal-time data relevant to that device such as location 204, on/offstatus 206, and utilization 208. An image of the device 210 isdisplayed, helping the user make sure they have selected the correctdevice or reminding them of what the device looks like should they needto find it within the facility. There is also a real-time feed of devicealerts 212 which notifies the user of data updates and actionsrecommended by the system. This user interface 200 is the chief methodby which the user is able to interact with the system, and it bothcommunicates system outputs to the user as well as relays user inputs tothe system. This means that, while not pictured in FIG. 2, the userinterface 200 is the means by which the user inputs equipmentinformation, maintenance preferences, maintenance records (see the userinput via the interface 1006 in FIG. 10). The user interface 200 thusacts as the interface between the user and the rest of the algorithm.The user is able to issue commands to the system using the userinterface. An example of this in FIG. 2 is the “LOCATE” button. If theuser presses this button, a command is sent from the data server 114 ausing the low-frequency band to the relevant beacon module 408,compelling the beacon module 408 to locate the particular asset.

An exemplary implementation of the system follows. In this scenario, aportable X-ray machine is outfitted with a plug module 502 and tagmodule 700. Most of the time it sits in a hallway powered down, but afew times a day it is used to scan patients. When this happens, the plugmodule 502 senses that electrical current is being drawn by the machineand it sends a wireless signal that the X-ray machine is in use. Thissignal is relayed through a handful of beacon modules 410 beforereaching the data server 114 a where this information is stored and thenforwarded to the remote data analysis algorithm over an internetconnection. When the X-ray machine is no longer in use, the plug module502 signals that the machine is now off and was in use for 1 hour. Thisinformation is also stored in the data server and forwarded to theremote data analysis algorithm. This continues for months with theremote data analysis algorithm 102 tracking the aggregate amount of timethe X-ray machine has been in use (i.e. total utilization). At any time,hospital engineering staff may check the on/off status of the X-raymachine, as well as its total utilization using the user interface whichis accessible by a networked device such as a tablet or computer.Eventually, total utilization reaches 300 hours—a utilization thresholdset by the engineering manager or gleaned from a database—and the dataanalysis algorithm 102 notifies engineering staff (through the userinterface 200) that routine maintenance is required by sending an alertto the user interface.

An engineer goes to perform this maintenance but cannot find the X-raymachine in the hallway where it is normally located. They access theuser interface on their smartphone and press the “LOCATE” button which,over an internet connection, sends a prompt to the data server which isthen wirelessly forwarded to the plug 502 and/or tag modules 700 on theX-ray machine. This prompt compels either module to wirelessly pollnearby beacon modules. Each beacon module 410 close enough to receivethis polling signal responds with a unique ID and the plug and/or tagmodule recognizes which response had the greatest signal strength andwirelessly reports the associated ID to the data server 114 a. Knowingwhich beacons are associated with which rooms, the data server convertsthis reported ID to a room number (which happens to be the ER in thisexample) and then communicates this over an internet connection to theuser via the user interface 200. The engineer now can see that theequipment is in the ER and goes there to perform routine maintenance.Two days later, a stretcher is accidentally slammed into the X-raymachine during an emergency. The tag module 700 detects the impact andimmediately alerts the engineering staff that an inspection is required.An engineer responds to this alert and finds that the machine case andsome internal components are damaged. He is able to take the X-raymachine out of use so that it can be repaired, avoiding a potentialmalfunction during use on a patient.

As should be clear from the scenario discussed above, the systemdisclosed herein offers a number of benefits. For example, by abandoninginefficient periodic maintenance paradigms in favor of the dynamic,condition-based maintenance recommendations made by the system, clinicalengineers can reduce time, effort, and money spent on maintenance taskswithout jeopardizing safety or asset quality. In addition, patientsafety will be improved due to the close monitoring of the behavior andenvironment of assets which enables hospital staff to catch failuresbefore they occur. The disclosed system also provides knowledge of thespecific conditions that have led to asset degradation informs engineersas to what specialized maintenance steps should be taken and which canbe skipped, reducing engineer effort and asset downtime. Moreover, thesystem provides real-time location tracking will save staff countlesshours searching for equipment.

The foregoing description and drawings should be considered asillustrative only of the principles of the invention. The invention maybe configured in a variety of shapes and sizes and is not intended to belimited by the preferred embodiment. Numerous applications of theinvention will readily occur to those skilled in the art. Therefore, itis not desired to limit the invention to the specific examples disclosedor the exact construction and operation shown and described. Rather, allsuitable modifications and equivalents may be resorted to, fallingwithin the scope of the invention.

1-16. (canceled)
 17. A system for monitoring an asset, comprising: oneor more asset-monitoring modules for monitoring the asset and providingmonitored data; and a processing device receiving the monitored data andrunning a data analysis algorithm, wherein said data analysis algorithmpredicts maintenance requirements for the asset; wherein the system isasset-agnostic, supporting multiple types and brands of assets and suchthat the performance of the assets can be managed and monitored inreal-time.
 18. The system of claim 17, wherein the data analysisalgorithm comprises a statistical model of performance for one or moreassets to be monitored.
 19. The system of claim 17, wherein the dataanalysis algorithm comprises a degradation model for the asset thatpredicts maintenance requirements or off-line statuses for said asset.20. The system of claim 17, wherein the data analysis algorithm appliesBayesian updating to perform real-time corrections in the performancemodel and degradation model.
 21. The system of claim 17, wherein saidone or more asset-monitoring modules comprise a tag module and/or plugmodule.
 22. The system of claim 17, wherein said one or moreasset-monitoring modules are comprised of one or more sensors thatmonitor environmental conditions, operational conditions and/or usageinformation related to the one or more assets.
 23. The system of claim17, further comprising one or more beacon modules broadcasting locationsignals to said one or more asset-monitoring modules, wherein said oneor more asset-monitoring modules determine a location based on receivedlocation signals.
 24. The system of claim 17, further comprising anequipment management dashboard that shows user alerts and/orrecommendations related to the one or more assets.
 25. The system ofclaim 17, wherein said one or more asset monitoring modules operateswirelessly.
 26. A method for monitoring an asset, comprising the stepsof: monitoring, at one or more asset-monitoring modules, an asset;providing monitored data to a processing device; receiving the monitoreddata at the processing device; and predicting, using a data analysisalgorithm, maintenance requirements for the asset; wherein the method isasset-agnostic, supporting multiple types and brands of assets and suchthat the performance of the one or more assets can be managed andmonitored in real-time.
 27. The method of claim 26, wherein the dataanalysis algorithm comprises a statistical model of performance for oneor more assets to be monitored.
 28. The method of claim 26, wherein thedata analysis algorithm comprises a degradation model for the asset thatpredicts maintenance requirements or off-line statuses for said asset.29. The method of claim 26, wherein the data analysis algorithm appliesBayesian updating to perform real-time corrections in the performancemodel and degradation model.
 30. The method of claim 26, wherein one ormore asset-monitoring modules comprise a tag module and/or plug module.31. The method of claim 26, wherein asset-monitoring modules arecomprised of a plurality of sensors that monitor environmentalconditions, operational conditions and/or usage information related tothe one or more assets.
 32. The method of claim 26, further comprisingone or more beacon modules broadcasting locator signals to said one ormore asset-monitoring modules, wherein the beacon modules providespatial location data on the asset.
 33. The method of claim 26, furthercomprising an equipment management dashboard that shows user alertsand/or recommendations related to the one or more assets.
 34. The methodof claim 26, wherein the one or more asset monitoring modules operateswirelessly.
 35. A system for monitoring one or more assets, comprising:one or more asset-monitoring modules for monitoring the asset andproviding monitored data; and a processing device receiving themonitored data and comparing the monitored data against previouslyrecorded data to determine an amount of usage of the asset; wherein thesystem is asset-agnostic, supporting multiple types and brands of assetsand such that the performance of the one or more assets can be managedand monitored in real-time.
 36. The system of claim 35, wherein theusage includes hours in use and/or modes of operation, the modes ofoperation including at least one of on, off high, medium, low, heating,or cooling.
 37. The system of claim 35, wherein said one or moreasset-monitoring modules are discrete modules that are separate from andcoupled to the asset.
 38. The system of claim 35, wherein the assetreceives power from a power supply, and wherein said one or moreasset-monitoring modules comprise a plug positioned between the powersupply and the asset to transfer power from the power supply to theasset and monitor the transferred power.
 39. The system of claim 35,wherein said one or more asset-monitoring modules each comprise asensor.
 40. The system of claim 39, said sensor comprising a currentsensor, voltage sensor, thermometer, accelerometer, gyroscope,hygrometer, and/or altimeter.
 41. The system of claim 35, wherein themonitored data comprises current, voltage, temperature, acceleration,orientation, humidity and/or pressure.